{"id":"https://openalex.org/W7160113357","doi":"https://doi.org/10.1109/jiot.2026.3689919","title":"Energy-Efficient Federated Learning Over Wireless Networks: A GNN-Assisted Deep Reinforcement Learning Approach","display_name":"Energy-Efficient Federated Learning Over Wireless Networks: A GNN-Assisted Deep Reinforcement Learning Approach","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160113357","doi":"https://doi.org/10.1109/jiot.2026.3689919"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3689919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3689919","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135250022","display_name":"Liang Wang","orcid":"https://orcid.org/0000-0002-5897-4401"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["School of Cybersecurity, Northwestern Polytechnical University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-1566-9546","affiliations":[{"raw_affiliation_string":"School of Cybersecurity, Northwestern Polytechnical University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043851737","display_name":"Zihao Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Wei","raw_affiliation_strings":["School of Cybersecurity, Northwestern Polytechnical University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0000-3627-5442","affiliations":[{"raw_affiliation_string":"School of Cybersecurity, Northwestern Polytechnical University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135252399","display_name":"Bomin Mao","orcid":"https://orcid.org/0000-0001-7780-5972"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bomin Mao","raw_affiliation_strings":["School of Cybersecurity, Northwestern Polytechnical University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-7780-5972","affiliations":[{"raw_affiliation_string":"School of Cybersecurity, Northwestern Polytechnical University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135120144","display_name":"Qu Luo","orcid":"https://orcid.org/0000-0001-6185-8375"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qu Luo","raw_affiliation_strings":["5G and 6G Innovation Centre, University of Surrey, Guildford, U.K"],"raw_orcid":"https://orcid.org/0000-0001-6185-8375","affiliations":[{"raw_affiliation_string":"5G and 6G Innovation Centre, University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011818025","display_name":"Qihao Peng","orcid":"https://orcid.org/0000-0002-7305-4646"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qihao Peng","raw_affiliation_strings":["5G and 6G Innovation Centre, University of Surrey, Guildford, U.K"],"raw_orcid":"https://orcid.org/0000-0002-7305-4646","affiliations":[{"raw_affiliation_string":"5G and 6G Innovation Centre, University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135185687","display_name":"Pei Xiao","orcid":"https://orcid.org/0000-0002-7886-5878"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Pei Xiao","raw_affiliation_strings":["5G and 6G Innovation Centre, University of Surrey, Guildford, U.K"],"raw_orcid":"https://orcid.org/0000-0002-7886-5878","affiliations":[{"raw_affiliation_string":"5G and 6G Innovation Centre, University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5211616,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"14","first_page":"32059","last_page":"32072"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9068999886512756,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9068999886512756,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.007799999788403511,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.0071000000461936,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.703499972820282},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7009999752044678},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.4900999963283539},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.38769999146461487},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.3075999915599823},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.3061000108718872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8428000211715698},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.703499972820282},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7009999752044678},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.4796999990940094},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.44440001249313354},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.38769999146461487},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2915000021457672},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3689919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3689919","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8515831828117371}],"awards":[{"id":"https://openalex.org/G1720326058","display_name":null,"funder_award_id":"62401469","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2333356826","display_name":null,"funder_award_id":"D5000250288","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Implementing":[0],"federated":[1],"learning":[2],"(FL)":[3],"over":[4,99],"wireless":[5,55,70,100,111],"networks":[6],"faces":[7],"critical":[8],"research":[9],"challenges,":[10],"such":[11],"as":[12],"high":[13],"communication":[14,17,79],"costs,":[15],"inevitable":[16],"latency,":[18],"and":[19,26,33,58,72,80,130,155,176],"significant":[20],"energy":[21,95,158,172],"consumption":[22,96,159],"for":[23,46],"model":[24,60,69,138],"transmission":[25],"training,":[27],"primarily":[28],"caused":[29],"by":[30],"device":[31],"heterogeneity":[32],"unpredictable":[34],"dynamic":[35],"channel":[36],"conditions.":[37],"This":[38],"paper":[39],"proposes":[40],"Graph-based":[41],"Resource":[42],"Optimization":[43],"with":[44,84],"Compression":[45],"FL":[47,161],"(GROC-FL),":[48],"a":[49,85,115,121,136,151],"unified":[50],"framework":[51],"that":[52,126,166],"jointly":[53],"coordinates":[54],"resource":[56,128],"allocation":[57],"collaborative":[59,137],"compression.":[61],"By":[62],"leveraging":[63],"Graph":[64],"Neural":[65],"Networks":[66],"(GNNs)":[67],"to":[68,77,104,149],"topology":[71,108],"Deep":[73],"Reinforcement":[74],"Learning":[75],"(DRL)":[76],"optimize":[78],"computation":[81],"resources":[82],"together":[83],"globally":[86],"consistent":[87],"sparse":[88,153],"update":[89],"mechanism,":[90],"GROC-FL":[91,167],"minimizes":[92],"the":[93,106,169],"overall":[94],"of":[97,110],"clients":[98,148],"networks.":[101],"In":[102],"order":[103],"address":[105],"intrinsic":[107],"dependence":[109],"FL,":[112],"we":[113],"develop":[114,135],"graph-augmented":[116],"DRL":[117],"agent":[118],"based":[119],"on":[120],"graph":[122],"convolutional":[123],"network":[124,131],"(GCN)":[125],"captures":[127],"competition":[129],"topology.":[132],"We":[133],"further":[134,156],"compression":[139],"module,":[140],"termed":[141],"Federated":[142],"Parameter":[143],"Negotiation":[144],"(FPN),":[145],"which":[146],"enables":[147],"negotiate":[150],"global":[152],"mask":[154],"reduce":[157],"during":[160],"training.":[162],"Experimental":[163],"results":[164],"demonstrate":[165],"outperforms":[168],"baselines":[170],"in":[171],"consumption,":[173],"training":[174],"performance,":[175],"client":[177],"fairness.":[178]},"counts_by_year":[],"updated_date":"2026-07-09T05:49:46.723101","created_date":"2026-05-05T00:00:00"}
